基于可穿戴设备的军校学员运动状态识别方法
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摘要
为准确识别军校学员便步、齐步、正步、跑步、仰卧起坐、俯卧撑、折返跑等七类日常运动状态,提出了一种基于可穿戴设备的军校学员运动状态识别方法。该方法首先利用穿戴式加速度数据采集设备采集并分析学员运动数据,并根据预先设定的特征,对运动数据进行特征提取;然后,利用设置的相应阈值对数据特征值进行识别,判断出相应的运动状态;最后,对军校学员日常运动中的七类运动状态进行了实验验证。结果表明:所提出的方法可有效识别学员运动状态,具有较高的识别率。
In an effort to accurately distinguish and test cadets′ daily motion states such as route step, quick march, goose step, running, sit-up, push-up, shuttle run etc., a motion state recognition method is proposed based on wearable device for cadets in military academy. First of all, a wearable acceleration data acquisition device is used to collect and analyze the trainees' movement data and extract the features of the data according to the predefined features. And then, the characteristic values are identified through the corresponding threshold to get the corresponding motion state. Finally, the seven kinds of daily motion state of cadets are tested and verified by experiments. The results show that the method proposed in this paper works effectively with a high rate of recognition.
In an effort to accurately distinguish and test cadets′ daily motion states such as route step, quick march, goose step, running, sit-up, push-up, shuttle run etc., a motion state recognition method is proposed based on wearable device for cadets in military academy. First of all, a wearable acceleration data acquisition device is used to collect and analyze the trainees' movement data and extract the features of the data according to the predefined features. And then, the characteristic values are identified through the corresponding threshold to get the corresponding motion state. Finally, the seven kinds of daily motion state of cadets are tested and verified by experiments. The results show that the method proposed in this paper works effectively with a high rate of recognition.
引文
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[2] 何卫华.人体行为识别关键技术研究 [D].重庆:重庆大学,2012.
[3] RIBEIRO B,ESPIRITO S A,CALIXTO W,et al.Development of a low power wireless network to support elderly people based on e Z430-Chronos and Simplici TI [C]//Education and Research Conference.s.l.:IEEE,2014.
[4] TROST S G,ZHENG Y,WONG W K.Machine learning for activity recognition:Hip versus wrist data [J].Physiol Meas.,2014,35(11):2183-2189.
[5] 涂亚庆,陈鹏,陈宝欣,等.基于离散特征的跌倒检测智能方法及应用 [J].仪器仪表学报,2017,38(3):629-634.TU Ya-qing,CHEN Peng,CHEN Bao-xin,et al.Intelligent fall detection method based on discrete feature and it′s application [J].Chinese Journal of Scientific Instrument,2017,38(3):629-634.(in Chinese)
[6] 胡云冰,童世华.一种便携式智能跌倒检测仪的设计与实现 [J].自动化仪表,2015(2):84-87.HU Yun-bing,TONG Shi-hua.Design and implementation of the portable intelligent fall detector [J].Automatic Instrument,2015(2):84-87.(in Chinese)
[7] 骆凯,罗军勇,尹美娟,等.一种基于动态阈值的突发流量异常检测方法 [J].信息工程大学学报,2016,17(4):509-512.LUO Kai,LUO Jun-yong,YIN Mei-juan,et al.Anomaly detection method for burst traffic based on dynamic threshold [J].Journal of Information Engineering University,2016,17(4):509-512.(in Chinese)
[8] 宋玲玲,李鹏,王瑞凤.基于加速度传感器的实时人体状态识别算法 [J].武汉理工大学学报(信息与管理工程版),2013,35(5):693-696.SONG Ling-ling,LI Peng,WANG Rui-feng.Algorithm of real-time human condition recognition based on acceleration sensor [J].Journal of WUT (Information & Management Engineering),2013,35(5):693-696.(in Chinese)
[9] 李娜,侯义斌,黄樟钦.基于人体加速度特征的实时跌倒识别算法 [J].小型微型计算机系统,2012,33(11):2410-2413.LI Na,HOU Yi-bin,HUANG Zhang-qin.Implementation of a real-time fall detection algorithm based on body′s acceleration [J].Journal of Chinese Computer Systems,2012,33(11):2410-2413.(in Chinese)
[10] KOBSAR D,OLSON C,PARANJAPE R,et al.The validity of gait variability and fractal dynamics obtained from a single body-fixed triaxial accele-rometer [J].Journal of Applied Biomechanics,2014,30(2):343-347.